Federated Learning Enhances CNC Tool Wear Prediction Across Distributed Data
A new arXiv paper (2608.11281) explores federated learning for predicting tool wear in CNC machining, addressing the challenge of distributed data that cannot be easily shared due to privacy or logistical constraints. The study simulates a federated scenario by distributing tool trajectories across simulated clients, then compares federated models against centralized references and local client baselines. Results indicate that federated learning achieves performance close to centralized learning while significantly outperforming local models. This approach enables collaborative model training without transferring raw operational data, making it suitable for industrial environments where data sharing is restricted. The paper highlights the potential of federated learning to improve tool condition monitoring, thereby supporting product quality and process reliability in CNC machining.
Key facts
- Paper arXiv:2608.11281 focuses on federated learning for CNC tool wear prediction.
- Tool wear prediction is critical for product quality and process reliability in CNC machining.
- Machine learning methods are limited by distributed data and sharing restrictions.
- Federated learning enables collaborative training without raw data transfer.
- Tool trajectories are distributed across simulated clients to mimic federated scenarios.
- Federated models perform close to centralized learning and better than local models.
- The study compares federated, centralized, and local client baselines.
- The approach is suitable for industrial environments with data privacy constraints.
Entities
Institutions
- arXiv